The Value of Residual Volume/Total Lung Capacity as an Indicator for Predicting Postoperative Lung Function in Non-Small Lung Cancer

Kwon, Oh-Beom; Yeo, Chang Dong; Lee, Hwa Young; Kang, Hye-Seon; Kim, Sung-Kyoung; Kim, Ju-Sang; Park, Chankwon; Lee, Sang-Haak; Kim, Seung-Joon; Kim, Jin-Woo

doi:10.3390/jcm10184159

Cited by 3 publications

(3 citation statements)

References 31 publications

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“…Lung cancer patients with COPD had preserved postoperative lung functions compared to patients without COPD. Therefore, the RV/TLC value was positively correlated with postoperative lung function [ 23 ]. We found that the RV/TLC value was also influential in predicting postoperative lung function with machine learning models.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Postoperative Lung Function in Lung Cancer Patients Using Machine Learning Models

Kwon¹,

Han²,

Lee³

et al. 2023

Tuberc Respir Dis

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Background: Surgical resection is the standard treatment for early-stage lung cancer. Since postoperative lung function is related to mortality, predicted postoperative lung function is used to determine the treatment modality. The aim of this study was to evaluate the predictive performance of linear regression and machine learning models. Methods:We extracted data from the Clinical Data Warehouse (CDW) and developed 3 sets: set Ⅰ, the linear regression model; set Ⅱ, machine learning models omitting the missing data: and set Ⅲ, machine learning models imputing the missing data. Six machine learning models, the least absolute shrinkage and selection operator (LASSO), ridge regression, ElasticNet, Random Forest, eXtreme gradient boosting (XGBoost), and the light gradient boosting machine (LightGBM) were implemented. The forced expiratory volume in one second (FEV1) measured 6 months after surgery was defined as the outcome. Five-fold cross-validation was performed for hyperparameter tuning of the machine learning models. The dataset was split into training and test datasets at a 70:30 ratio. Implementation was done after dataset splitting in set Ⅲ. Predictive performance was evaluated by R 2 and mean squared error (MSE) in the 3 sets.Results: A total of 1,487 patients were included in sets Ⅰ and Ⅲ and 896 patients were included in set Ⅱ. In set Ⅰ, the R 2 value was 0.27 and in set Ⅱ, LightGBM was the best model with the highest R 2 value of 0.5 and the lowest MSE of 154.95. In set Ⅲ, LightGBM was the best model with the highest R 2 value of 0.56 and the lowest MSE of 174.07. Conclusion:The LightGBM model showed the best performance in predicting postoperative 4 lung function.

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Section: Discussionmentioning

confidence: 99%

Prediction of Postoperative Lung Function in Lung Cancer Patients Using Machine Learning Models

Kwon¹,

Han²,

Lee³

et al. 2023

Tuberc Respir Dis

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“…Choosing SBRT instead of surgery is mostly due to clinico-pathological criteria, but a certain amount of patients with NSCLC might be intolerant to surgical resections due to impaired pulmonary function. Possible extensions include pulmonary function tests such as FOT, which provide information about lung capacity and tissue mechanics [36,54]. Measurements of lung function with non-invasive devices (i.e., FOT) in fragile patients with lung cancer would help to determine the functional consequences of irradiating small fields in lungs and establish evidence based criteria for eligibility for SBRT schemes on the basis of lung function.…”

Section: Discussionmentioning

confidence: 99%

Model Calibration of Pharmacokinetic-Pharmacodynamic Lung Tumour Dynamics for Anticancer Therapies

Ghita

Billiet

Copot

et al. 2022

JCM

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Individual curves for tumor growth can be expressed as mathematical models. Herein we exploited a pharmacokinetic-pharmacodynamic (PKPD) model to accurately predict the lung growth curves when using data from a clinical study. Our analysis included 19 patients with non-small cell lung cancer treated with specific hypofractionated regimens, defined as stereotactic body radiation therapy (SBRT). The results exhibited the utility of the PKPD model for testing growth hypotheses of the lung tumor against clinical data. The model fitted the observed progression behavior of the lung tumors expressed by measuring the tumor volume of the patients before and after treatment from CT screening. The changes in dynamics were best captured by the parameter identified as the patients’ response to treatment. Median follow-up times for the tumor volume after SBRT were 126 days. These results have proven the use of mathematical modeling in preclinical anticancer investigations as a potential prognostic tool.

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“…A meta-analysis has shown that CT volume and density measures are the preferred method for predicting postoperative FEV 1 [6]. In our previous study [7], we showed that preoperative residual volume (RV)/total lung capacity (TLC) is positively correlated with postoperative lung function. As a result, in addition to the number of segments removed, lung function should also be taken into account because the concurrent respiratory condition affects postoperative lung function.…”

Section: Introductionmentioning

confidence: 99%

Predicting Postoperative Lung Function in Patients with Lung Cancer Using Imaging Biomarkers

Kwon,

Lee,

Park

et al. 2024

Diseases

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There have been previous studies conducted to predict postoperative lung function with pulmonary function tests (PFTs). Computing tomography (CT) can quantitatively measure small airway walls’ thickness, lung volume, pulmonary vessel volume, and emphysema area, which reflect the severity of respiratory diseases. These measurements are considered imaging biomarkers. This study aimed to predict postoperative lung function with imaging biomarkers. A retrospective analysis of 79 patients with lung cancer who had undergone lung surgery was completed. Postoperative lung function measured by forced expiratory volume in one second (FEV1) was defined as an outcome. Preoperative clinico-pathological parameters and imaging biomarkers representing airway walls’ thickness, severity of emphysema, total lung volume, and pulmonary vessel volume were measured quantitatively in chest CT by an automated segmentation software, AVIEW COPD. Pi1 was defined as the first percentile along the histogram of lung attenuation that represents the degree of emphysema. Wafw was defined as the airway thickness, which was calculated by the full-width at half-maximum method. Logistic and linear regressions were used to assess these variables. If the actual postoperative FEV1 was higher than the postoperative FEV1 projected by a formula, the group was considered to be preserved. Among the 79 patients, 16 of the patients were grouped as a non-preserved group, and 63 of them were grouped as a preserved group. The patients in the preserved FEV1 group had a higher vessel volume than the non-preserved group. Pi1 and Wafw were independent predictors of postoperative lung function. Imaging biomarkers can be considered significant variables in predicting postoperative lung function in patients with lung cancer.

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The Value of Residual Volume/Total Lung Capacity as an Indicator for Predicting Postoperative Lung Function in Non-Small Lung Cancer

Cited by 3 publications

References 31 publications

Prediction of Postoperative Lung Function in Lung Cancer Patients Using Machine Learning Models

Prediction of Postoperative Lung Function in Lung Cancer Patients Using Machine Learning Models

Model Calibration of Pharmacokinetic-Pharmacodynamic Lung Tumour Dynamics for Anticancer Therapies

Predicting Postoperative Lung Function in Patients with Lung Cancer Using Imaging Biomarkers

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